mirror of
https://github.com/comfyanonymous/ComfyUI.git
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57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
from __future__ import annotations
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import torch
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from comfy_api.v3 import io
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class InstructPixToPixConditioning(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="InstructPixToPixConditioning_V3",
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category="conditioning/instructpix2pix",
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inputs=[
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io.Conditioning.Input("positive"),
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io.Conditioning.Input("negative"),
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io.Vae.Input("vae"),
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io.Image.Input("pixels"),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, positive, negative, pixels, vae):
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
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concat_latent = vae.encode(pixels)
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out_latent = {}
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out_latent["samples"] = torch.zeros_like(concat_latent)
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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d["concat_latent_image"] = concat_latent
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return io.NodeOutput(out[0], out[1], out_latent)
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NODES_LIST = [
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InstructPixToPixConditioning,
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]
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